Linear Single-Antenna Interference Cancellation Receiver

ABSTRACT

System and method for interference cancellation in a digital wireless communications system. A preferred embodiment comprises sampling a received signal wherein the received signal is real-valued, rotating the sampled received signal by a specified amount, extracting in-phase and quadrature phase streams from the rotated, sampled received signal, applying an interference suppression filter and combining the filtered streams. The output of the combining operation can be de-correlated (by whitening) if there is excessive correlation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This applciation is a Continuation of application Ser. No. 10/747,461filed Dec. 29, 2003, which claims the benefit of U.S. ProvisionalApplication No. 60/458,245, filed Mar. 28, 2003, entitled “LinearSingle-Antenna Interference Cancellation Receiver for GSM Systems”, andApplication No. 60/509,891, filed Oct. 09, 2003, entitled “Another BlindSingle-Antenna Interference Cancellation Receiver for GSM Systems” whichapplications are hereby incorporated herein by reference.

This application is related to the following co-pending and commonlyassigned patent applications Ser. No. 10/738,508, filed Dec. 17, 2004,entitled “Interferer Detection and Channel Estimation for WirelessCommunications Networks,” Ser. No. 10/732,978, filed Dec. 11, 2003,entitled “Multiuser Detection for Wireless Communications Systems in thePresence of Interference,” which applications are hereby incorporatedherein by reference.

TECHNICAL FIELD

The present invention relates generally to a system and method fordigital wireless communications, and more particularly to a system andmethod for providing interference cancellation in a digital wirelesscommunications system.

BACKGROUND

Interference is a major source of concern for the designers of wirelesscommunications systems. Interference can reduce the overall performanceof the communications system and if severe enough, cause thecommunications system to fail altogether. Interference can come fromother electrical and electronic devices operating in the generalvicinity and from other devices in the same communications system, whichare transmitting in the same (or adjacent) frequency band.

Interference from other devices in the same communications system canbecome a problem as designers of the communication system attempt toincrease network capacity. For example, one way to increase networkcapacity is to increase frequency reuse, i.e., allow devices that arerelatively close to one another to transmit in the same frequency band.In cellular communications systems, adjacent cell sites typically do notoperate in the same frequency bands. However, through cell sitesectoring, frequency reuse can be increased, therefore increasingnetwork capacity. Unfortunately, when devices, which are close to oneanother, transmit in the same frequency band or in adjacent frequencybands, interference can occur. When devices transmit within the samefrequency band, co-channel interference can occur, while adjacentchannel interference can occur if devices transmit in adjacent bands ifsufficient interband spacing is not provided.

Additionally, when multiple users are transmitting, the information maybecome mixed together and it may be necessary to extract one (or more)user's information from a received signal. For receivers with multipleantennas, linear schemes can be used to extract the desired information.The use of linear schemes in receivers with single antennas may bedifficult if not impossible without the aid of additional signalmanipulation.

In a GSM (Global System for Mobile Telephony) wireless communicationssystem, for example, information is transmitted in transmission bursts,wherein each transmission burst may consist of two packets of data bitswith a 26 bit mid-amble located in between the two packets. According tothe GSM technical standards, one of eight possible training sequencecodes (TSC) can be used as the mid-amble. In GSM communications systems,attempts to increase system capacity have resulted in increasedco-channel and adjacent channel interference. Several attempts to reduceinterference have been proposed. Most of the prior art relies on usingat least two antennas at the receiver to suppress interference. However,due to cost reasons there is generally only one antenna in GSM handsets.With a single antenna at the receiver, one single antenna interferencecancellation (SAIC) technique is to use the joint MLSE receiver.

A disadvantage of the prior art is that the schemes which providesignificant performance gain require the channel information of theinterferers. This may not be available since in general the identity ofthe interferers is unknown. In a synchronous network, this may requirean algorithm capable of detecting the presence and identity of theinterferer(s). In an asynchronous network, attaining such information isgenerally infeasible.

SUMMARY OF THE INVENTION

These and other problems are generally solved or circumvented, andtechnical advantages are generally achieved, by preferred embodiments ofthe present invention which provides for a system and method forproviding interference cancellation in wireless communications systems.

In accordance with a preferred embodiment of the present invention, amethod for eliminating interference in a received signal comprisingsampling the received signal, rotating the sample received signal by aspecified amount, extracting in-phase and quadrature phase streams fromthe rotated, sampled received signal, applying an interferencesuppression filter to the in-phase and quadrature phase streams, andcombining the filtered in-phase and quadrature phase streams isprovided.

In accordance with another preferred embodiment of the presentinvention, a method for eliminating interference in a received signalcomprising sampling the received signal, wherein the sampling is at asampling rate that is not less than a symbol rate of the receivedsignal, rotating the sampled received signal by a specified amount,extracting in-phase and quadrature phase streams from the rotated,sampled received signal, and applying an interference suppression filterto the in-phase and quadrature phase streams is provided.

In accordance with another preferred embodiment of the presentinvention, a circuit comprising a sampling unit coupled to a signalinput, the sampling unit containing circuitry to sample a receivedsignal provided by the signal input at a specified sampling rate, arotating unit coupled to the sampling unit, the rotating unit containingcircuitry to rotate the sample received signal by a specified amount, apair of extractors coupled to the rotating unit, the extractorscontaining circuitry to extract an in-phase and a quadrature phasestream from an output of the rotating unit, and a filter coupled to thepair of extractors, the filter containing circuitry to suppressinterference present in the received signal is provided.

An advantage of a preferred embodiment of the present invention is theinterference cancellation can make use of single antenna receivers,therefore, existing receivers can be used.

A further advantage of a preferred embodiment of the present inventionis that spectral redundancy available in many modulation schemes can beexploited to provide an additional degree of freedom to assist ininterference cancellation. The additional degree of freedom caneffectively operate as a “virtual” antenna to make a single antennareceiver behave like a two antenna receiver.

Yet another advantage of a preferred embodiment of the present inventionis that further technique can be used to provide greater degrees offreedom. These additional degrees of freedom can add additional“virtual” antennas to a single antenna receiver, permitting the use ofinterference cancellation techniques that typically require a largenumber of antennas.

Yet another advantage of a preferred embodiment of the present inventionis that implementation can be achieved without requiring the channelinformation and identity of the interferers. Only the desired userchannel information is used. Hence, the techniques are applicable forasynchronous networks.

The foregoing has outlined rather broadly the features and technicaladvantages of the present invention in order that the detaileddescription of the invention that follows may be better understood.Additional features and advantages of the invention will be describedhereinafter which form the subject of the claims of the invention. Itshould be appreciated by those skilled in the art that the conceptionand specific embodiment disclosed may be readily utilized as a basis formodifying or designing other structures or processes for carrying outthe same purposes of the present invention. It should also be realizedby those skilled in the art that such equivalent construction do notdepart from the spirit and scope of the invention as set forth in theappended claims

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention, and theadvantages thereof, reference is no made to the following descriptionstaken in conjunction with the accompanying drawings, in which:

FIG. 1 is a diagram of a transmission burst in a GSM communicationssystem;

FIG. 2 is a diagram of a detailed view of the 26-bit GSM trainingsequence field;

FIG. 3 is a diagram of the transmissions from three GSM devices with notiming offset;

FIG. 4 is a diagram of a portion of a receiver, according to a preferredembodiment of the present invention;

FIG. 5 is a diagram of a process for interference cancellation in areceiver, according to a preferred embodiment of the present invention;

FIG. 6 is a diagram of a portion of a receiver, according to a preferredembodiment of the present invention;

FIG. 7 is a diagram of a receiver circuit, according to a preferredembodiment of the present invention;

FIG. 8 is a diagram of a process for interference cancellation in areceiver, according to a preferred embodiment of the present invention;

FIG. 9 is a data plot of link level performance for several singleantenna interference cancellation algorithms, according to a preferredembodiment of the present invention; and

FIG. 10 is a data plot of link level performance for several singleantenna interference cancellation algorithms, according to a preferredembodiment of the present invention.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The making and using of the presently preferred embodiments arediscussed in detail below. It should be appreciated, however, that thepresent invention provides many applicable inventive concepts that canbe embodied in a wide variety of specific contexts. The specificembodiments discussed are merely illustrative of specific ways to makeand use the invention, and do not limit the scope of the invention.

The present invention will be described with respect to preferredembodiments in a specific context, namely a GSM network operating insynchronous mode. The GSM technical standard can be found in a series oftechnical documents, wherein a general description can be found inDocument 01,02, entitled “General Description of GSM Public Land MobileNetwork (PLMN), Revision 6.0.0” published January 2001, which isincorporated herein by reference. The invention may also be applied,however, to other wireless communications networks which make use ofreal-valued modulation schemes such as GMSK, BPSK, and M-PAM. Examplesof these networks include but are not limited to GSM-EDGE, GPRS, and soon.

With reference now to FIG. 1, there is shown a diagram illustrating atransmission burst 100 in a GSM communications system. Data transmittedin the transmission burst 100 are carried in a pair of 57-bit datafields 105. Two 3-bit fields, referred to as tail bit fields 110, can beused to keep adjacent transmission bursts separate. In many wirelesscommunications systems, transmissions are usually preceded with a fieldlocated at the beginning of the transmission. This field is commonlyreferred to as a preamble and can be used to carry a specific sequenceof bits (typically referred to as a training sequence) that can help areceiver detect and decode the transmission. Note that while the use ofa preamble is common, it is not the only place within in a transmissionto place a training sequence. For example, in a GSM transmission burst,the training sequence is located in the middle of the transmissionburst. The transmission burst 100 contains a 26-bit training sequencefield 115, which may be separated from the pair of 57-bit data fields105 by a pair of stealing bit fields 120. Since the training sequence isnot at the beginning of the transmission, it referred to as being amid-amble. Note that the discussion of field specifies (the number ofbits in a field, the position of a field, and so forth) is used toenable the discussion using a currently available wirelesscommunications system. It should be evident that the field specifiesshould have no impact upon the validity of the present invention.

With reference now to FIG. 2, there is shown a diagram illustrating adetailed view of the GSM 26-bit training sequence field 115. The GSM26-bit training sequence field 115 can be broken up into three smallerfields, a 5-bit cyclic prefix field 205, a 16-bit training sequencefield 210, and a 5-bit cyclic postfix field 215. According to the GSMtechnical standards, the 5-bit cyclic prefix field 205 contains a copyof the last 5 bits of the 16-bit training sequence field 210 while the5-bit cyclic postfix field 215 contains a copy of the first 5 bits ofthe 16-bit training sequence field 210. According to the GSM technicalspecifications, there are up to eight (8) unique training sequences thatmay be used in a signal GSM communications system.

As discussed previously, interference from other devices from within thesame communications network can come in two forms, co-channel andadjacent channel interference. Regardless of the form of interference,the net result may be that the overall performance of the source of theinterference and receiver of the interference may be degraded since thetransmissions of both the device causing the interference and the devicebeing interfered with are being damaged. Since the number of trainingsequences is limited (eight in the case of a GSM communication system),it can be possible to use the a priori knowledge of the trainingsequences to improve the channel estimation performance at a receiver.

With reference now to FIG. 3, there is shown a diagram illustrating thetransmissions of three GSM devices, wherein there is no timing offset.Each of three acts of axes (305, 310, and 315) display a series of GSMtransmission bursts from a single device. Note that each device uses adifferent training sequence; TSC0 for the transmission displayed on axis305, TSC2 for the transmission displayed or axis 310, and TSC1 for thetransmission displayed on axis 315. Note that the GSM communicationssystem displayed in FIG. 3 is a synchronous system, wherein all of thedevices transmit at essentially the same time. For example, first GSMtransmission bursts 307, 312, and 317 are all transmitted at the sametime, as are second GSM transmission bursts 308, 313, and 318. Also notethat there is no (or less than a single symbol) timing offset betweenthe transmissions of the three devices. A vertical line 320 denotes thebeginning of the second GSM transmission bursts 308, 313, and 318 in allthree devices.

Note that it may be possible that a timing offset exists between thearrival times of transmissions from different devices. A timing offsetmay exist even if transmissions within a wireless communications systemare designed to occur at the same time. For example, if a clock of atransmitter has drifted away from clocks of other transmitters, then thetransmitter with the inaccurate clock can begin its transmission at anincorrect time. Alternatively, differences in the distance traveled byvarious transmissions (propagation delay) can also account for a timingoffset. For example, even if transmissions are initiated at the sametime, a transmission that is traveling a long distance will arrive laterthan a transmission that is traveling a short distance. A timing offsetcan vary from nanoseconds to milliseconds. When a timing offset islarge, it can sometimes be expressed in terms of symbol intervals (anamount of time equal to the transmission of a single symbol).

With reference now to FIG. 4, there is shown a diagram illustrating aportion of a receiver 400, wherein addition signal manipulation can beused to provide an additional degree of freedom to assist ininterference cancellation, according to a preferred embodiment of thepresent invention. FIG. 4 provides a high-level view of circuitryresponsible for interference cancellation in the receiver 400. Note thatFIG. 4 does not show typical parts that may be found in a receiver, suchas an antenna, radio frequency hardware, decoding hardware, and soforth. A received signal, r(t), as received by an antenna (not shown) ofthe receiver 400 and after filtering to remove out-of-band interferersand amplifying to bring signal levels to a compatible level by radiofrequency hardware (also not shown), may be sampled at baud-rate by abaud-rate sampling unit 405. The sampling of the received signal createsa discrete time sequence representing the received signal. After thesampling, the discrete time sequence can be provided to a linearinterference suppression unit 410. According to another preferredembodiment of the present invention, sub-baud rate processing can beused in place of baud-rate processing.

The linear interference suppression unit 410 can include a derotationunit 415, which can be used to apply a specified rotation to thediscrete time sequence. After derotation, the discrete time sequence canbe provided to a real-valued unit 420 and an imaginary-valued unit 422,which can be responsible for extracting the real portion and theimaginary portion of the discrete time sequence, e.g., the discrete timesequence can be split into two sequences, with one sequence containingthe real portion of the discrete time sequence and the other sequencecontaining the imaginary portion of the discrete time sequence. The realsequence can represent an in-phase portion and the imaginary sequencecan represent a quadrature portion of the discrete time sequence.

Each of the two sequences (the real sequence and the imaginary sequence)can then undergo filtering via two filter units 425 and 427. A detailedexplanation of the operation of the filter units 425 and 427 is providedbelow. After filtering, the two sequences can be recombined by acombining unit 430. A net effect of the filtering by the filter units425 and 427 and the combining unit 430 is interference suppression.After combining, the output may be colored. If the coloring is severe,then the output may be whitened (de-correlated) by a whitening unit 435prior to minimum least squares error equalization.

For discussion purposes, a signal model, along with assumptions andnotation shall be laid out. Note that the signal model presented belowis for a GSM communications system. However, a comparable signal modelcan be provided for other types of communications systems. A basebandreceived signal can be sampled at a baud (symbol) rate to facilitatedisrete-time processing. A Gaussian Minimum Shift Keying (GSSK)modulated signal can be accurately approximated using a linearapproximation expressible as:${{x(t)} = {\sum\limits_{p = {- \infty}}^{\infty}\quad{j^{p + 1}a_{p}{C_{0}\left( {t - {pT}} \right)}}}},{a_{p} \in \left\{ {\pm 1} \right\}},$where T is a single symbol duration and C₀(t) is the GMSK waveform ofduration 47.

Assuming that there are {tilde over (K)} co-channel users in thecommunications system, the baseband receive signals can be expressed as:${{\overset{\sim}{r}(t)} = {{\sum\limits_{k = 1}^{\overset{\sim}{K}}\quad{\sum\limits_{p = {- \infty}}^{\infty}\quad{j^{p + 1}a_{k,p}{{\overset{\sim}{h}}_{k}\left( {t - {pT}} \right)}}}} + {\overset{\sim}{n}(t)}}},{{\overset{\sim}{r}}_{m} = {{\overset{\sim}{r}({mT})} = {{j^{m + 1}{\sum\limits_{k = 1}^{\overset{\sim}{K}}\quad{\sum\limits_{l = 0}^{L}\quad{\left( {j^{- 1}{\overset{\sim}{h}}_{k,l}} \right)a_{k,{m - l}}}}}} + {\overset{\sim}{n}}_{m}}}},$where {tilde over (h)}(t) is the overall channel impulse responseincluding C₀(t), {tilde over (h)}_(i)={tilde over (h)}(IT), and LT isthe channel delay spread.

With reference now to FIG. 5, there is shown a diagram illustrating aprocess 500 for interference cancellation in a receiver, whereinaddition signal manipulation can be used to provide an additional degreeof freedom to assist in interference cancellation, according to apreferred embodiment of the present invention. A received signal, r(t),after being received by a receiver and sampled (block 505) at a samplingrate essentially equal to the received signal's baud rate can beexpressed as:${\overset{\sim}{r}}_{m} = {{\overset{\sim}{r}({mT})} = {{j^{m + 1}{\sum\limits_{k = 1}^{\overset{\sim}{K}}\quad{\sum\limits_{l = 0}^{L}\quad{\left( {j^{- 1}{\overset{\sim}{h}}_{k,l}} \right)a_{k,{m - l}}}}}} + {\overset{\sim}{n}}_{m}}}$Note that a_(k) can be modulated using binary phase shift keying (BPSK)and therefore is real-valued.

Two independent real-valued channels can be obtained from {tilde over(r)}_(m) by first performing a derotation (block 510) with a factor of(−j)^(m+1) followed by extracting a real (in-phase) and imaginary(quadrature) part of the resulting signal (block 515). The twoindependent real-valued channels can be expressed in vector form as:$\begin{matrix}{\begin{bmatrix}{{Re}\left( {j^{- {({m + 1})}}{\overset{\sim}{r}}_{m}} \right)} \\{{Im}\left( {j^{- {({m + 1})}}{\overset{\sim}{r}}_{m}} \right)}\end{bmatrix} = {\left. {{\sum\limits_{k = 1}^{K}\quad{\sum\limits_{l = 0}^{L}{\begin{bmatrix}{{Re}\left( {j^{- l}{\overset{\sim}{h}}_{l}^{(k)}} \right)} \\{{Im}\left( {j^{- l}{\overset{\sim}{h}}_{l}^{(k)}} \right)}\end{bmatrix}a_{m - l}^{(k)}}}} + \begin{bmatrix}{{Re}\left( {j^{- {({m + 1})}}{\overset{\sim}{n}}_{m}} \right)} \\{{Im}\left( {j^{- {({m + 1})}}{\overset{\sim}{n}}_{m}} \right)}\end{bmatrix}}\Leftrightarrow{\overset{\sim}{r}}_{m} \right. = {{{\sum\limits_{k = 1}^{K}\quad{\sum\limits_{l = 0}^{L}{h_{l}^{(k)}a_{m - l}^{(k)}}}} + {\overset{\sim}{n}}_{m}} = {{\sum\limits_{k = 1}^{K}{\begin{bmatrix}h_{\quad 0}^{(k)} & {\quad h_{\quad 1}^{(k)}} & \ldots & h_{\quad L}^{(k)}\end{bmatrix}a_{m - l}^{(k)}}} + {{\overset{\sim}{n}}_{m}.}}}}} & (1)\end{matrix}$

This effectively provides a single-input 2-output real-valued channel.Essentially, the spectral redundancy stemming from the fact that a_(k)is a real-valued symbol is exploited. It can be shown that the twoindependent real-valued channels (shown above) are uncorrelated from oneanother. The same holds true for the noise components. Note that theGMSK-specific feature above may arise from the GMSK waveform, C₀(t), andthe rotation (−j)^(m+1). The above technique of exploiting spectralredundancy can also be applicable to any general real-valued modulationscheme such as BPSK and M-PAM. For BPSK and M-PAM, the derotation (forexample, as performed by the derotation unit 415 (FIG. 4)) may not beneeded. In this case, the sampled received signal can be directlyprocessed by in-phase and quadrature extractors (for example, thereal-valued unit 420 and an imaginary-valued unit 422 (FIG. 4)).

The additional degree of freedom (achieved by exploiting the spectralredundancy and creating two independent real-valued channels from thereceived signal) can be used to fully suppress a single interferer.Alternatively, if there is more than one interferer, then the singledegree of freedom can be used to partially suppress the multipleinterferers. The interference suppression can be performed by filteringthe two independent real-valued channels and then combining the resultsof the filtering (block 520). The filters (w₁(z) and w₂(z)) can have N+1taps which can result in an increase in the number of states for theMLSE equalizer by a factor of 2^(N) times. A detailed discussion of thefilters (w₁(z) and w₂(z)) is provided below.

The filters (w₁(z) and w₂(z)) can be chosen to suppress interference.Several different criteria can be used, such as, zero-forcing (ZF),minimum mean square error (MMSE), and maximum signal to interferenceplus noise ratio (SINR). The following describes a design of the filtersusing a maximum SINR criteria. Proceeding from equation (1) above, setthe number of taps for each filter to N+1 and let: $\begin{matrix}{\quad\begin{matrix}{\quad{{{r_{m} = {\begin{bmatrix}{\overset{\sim}{r}}_{m} \\{\overset{\sim}{r}}_{m - 1} \\\vdots \\{{\overset{\sim}{r}}_{m} - N}\end{bmatrix} \in \Re^{2{({N + 1})}}}},{n_{m} = {\begin{bmatrix}{\quad{\quad\overset{\sim}{n}}_{m}} \\{\quad{\quad\overset{\sim}{n}}_{m\quad - \quad 1}} \\\vdots \\{\quad{\quad\overset{\sim}{n}}_{m\quad - \quad n}}\end{bmatrix} \sim {{RealGaussian}\left\lbrack {O_{2{({N + 1})}},{\frac{\sigma^{2}}{2}I_{2{({N + 1})}}}} \right\rbrack}}},{a_{m}^{(k)} = {\begin{bmatrix}a_{m}^{(k)} \\a_{m - 1}^{(k)} \\\vdots \\a_{m - L - N}^{(k)}\end{bmatrix} \in {BPSK}^{L + N + 1}}}}{{Then},{r_{m} = {{{{\sum\limits_{k = 1}^{K}\quad{H_{k}a_{m}^{(k)}}} + n_{m}}=={{H_{1}a_{m}^{(1)}} + {\sum\limits_{k = 2}^{K}{H_{k}\quad a_{m}^{(k)}}} + n_{m}}} = \quad{{H_{1}a_{m}^{(1)}} + v_{m}}}}}}} & \quad\end{matrix}} & (2)\end{matrix}$where H_(k) is the 2(N+1)×(L+N+1) block Toeplitz matrix formed from {h₀^((k)),h₁ ^((k)), . . . ,h_(L) ^((k))}.

The combined filter (combining both filters w₁(z) and w₂(z)) w=[w_(1,0)w_(2,0 w) _(1,1) w_(2,1) . . . w_(1,N) w_(2,N)]^(T)ε

^(2(N+1)) operates upon the two independent real-valued channels toproduce a single stream output as follows:y _(m) =w ^(T) r _(m) =w ^(T) H ₁ a _(m) ⁽¹⁾ +w ^(T)ν_(m) =u ^(T) a _(m)⁽¹⁾ +w ^(T)ν_(m)  (3)where u denotes the effective desired user channel after interferencesuppression. In this case, the SINR can be defined as: $\begin{matrix}{{SINR} = {\frac{E\left\lbrack \left( {u^{T}a_{m}^{(1)}} \right)^{2} \right\rbrack}{E\left\lbrack \left( {{w^{T}r_{m}} - {u^{T}a_{m}^{(1)}}} \right)^{2} \right\rbrack}.}} & (4)\end{matrix}$It can be shown that a SINR-maximizing solution is expressible as:$\begin{matrix}{{{w = {R_{{rr}\quad}^{- 1}H_{1}u}},{u = {{evec}_{\max}\left\lbrack {H_{1}^{T}R_{rr}^{- 1}H_{1}} \right\rbrack}}}{R_{rr}^{- 1} = {{E\left\lbrack {r_{m}r_{m}^{T}} \right\rbrack} = {{\sum\limits_{k = 1}^{K}\quad{H_{k}H_{k}^{T}}} + {\frac{\sigma^{2}}{2}I_{2{({N + 1})}}}}}}} & (5)\end{matrix}$where evec_(max)[X] denotes the eigenvector for matrix X correspondingto the maximum eigenvalue. Note that by definition in equation (3),u=H₁ ^(T)w.  (6)

When channel estimates of all K users are available (e.g., via jointleast-squares channel estimation and knowledge of training sequences forthe users), the above solution can simply use the channel estimates toderive an optimal combining filter. In blind implementation, where onlya channel estimate of the desired user is available (e.g., via a singleuser channel estimation), the data covariance matrix, C_(rr), can beestimated via the sample covariance matrix by averaging multiplesnapshots of r_(m)r_(m) ^(T).

The solution discussed above can permit a blind adaptive implementationusing a wide variety of adaptive filtering techniques. This can bepossible by noting that for a given u, w is essentially a linear MMSEfilter that minimizes the mean square error in the denominator of SINR(equation (4)). The solution may begin with an initial estimate for u,then adaptively obtain w, and then refine u using equation (6). This maybe done iteratively. An adaptive implementation can be beneficial,especially for time-varying channels (wherein the channel variessignificantly within one transmission burst) and highly asynchronousnetworks (wherein the interferers and hence the interferer's channelsand statistics change within a transmission burst).

Using the training code sequence of the desired user, it can also bepossible to obtain a SINR-maximizing solution, wherein SINR is definedin a deterministic sense, rather than a stochastic sense as in equation(4). Collecting M snapshots of r_(m) in equation (2) within a period ofthe training code sequence (for a GSM communications system, thetraining code sequence is made up of 26 symbols per transmission burst),[r ₀ r ₁ . . . r _(M-i) ]=H ₁ └a ₀ ⁽¹⁾ a ₁ ⁽¹⁾ . . . a _(M-1) ⁽¹⁾┘+[ν₀ν₁ . . . ν_(M-1)]

R=H ₁ A ₁ +V.  (7)Similar to the stochastic approach, the combining filter can operate asfollows:y ^(T) =w ^(T) R=w ^(T) H ₁ A ₁ +w ^(T) V=u ^(T) A ₁ +w ^(T) V.The deterministic SINR can then be defined as: $\begin{matrix}{{SINR} = {\frac{{{u^{T}A_{1}}}^{2}}{{{{w^{T}R} - {u^{T}A_{1}}}}^{2}}.}} & (8)\end{matrix}$It can be shown that the SINR-maximizing solution in this case is:w=(RR ^(T))⁻¹ RA ₁ ^(T) u, u=evec_(max)[(A ₁ A ₁ ^(T))⁻¹ A ₁ R ^(T)(RR^(T))⁻¹ RA ₁ ^(T)].  (9)It can be shown that the above solution can be equivalent to:w=evec_(max)[(RR ^(T))⁻¹ RA ₁ ^(T)(A ₁ A ₁ ^(T))⁻¹ A ₁ R ^(T)]u=(A ₁ A ₁ ^(T))⁻¹ A ₁ R ^(T) w=Ĥ ₁ ^(T) w,Wherein Ĥ₁ is the single user least square channel estimate of thedesired user. Therefore, if a better channel estimate can be obtainedvia a different method, it can be used in place of Ĥ₁. Additionally,this shows that channel estimation can be performed separately from SINRmaximization.

After interference suppression (filtering and combining) and beforebeing provided to an MLSE equalizer, it may be necessary to whiten(de-correlate) the interference suppressed signal (block 525). Theinterference suppressed signal can be severely colored (as a result ofthe filtering and combining operations). If this is the case, then theinterference suppressed signal can be whitened (for example, by awhitening unit 435 (FIG. 4)). Severe coloring can impact the performanceof MLSE. The whitening can be implemented adaptively using linearprediction filtering. Alternatively, it can be shown that thecorrelating function of the residual interference (plus noise) takes thefollowing form:${{C(\Delta)} = {{w^{T}\left( {{\sum\limits_{k = 2}^{K}\quad{H_{k}D_{\Delta}H_{k}^{T}}} + {\frac{\sigma^{2}}{2}\delta_{\Delta}I_{2{({N + 1})}}}} \right)}w}},$wherein D_(A) is the shifted identity matrix. A whitening filter canthen be derived using spectral factorization. Note that the process 500may not be limited to co-channel interference suppression alone, theprocess 500 can also suppress adjacent channel interference. Notice thatthe above correlation function depends on the interferer(s)'s channelinformation. Alternatively, the whitening filter can be derived from alinear prediction formulation where the interference is modeled as anauto-regressive (AR) process with order-N, where (N+1) is the whiteningfilter length. Using this formulation, the whitening filter can bederived from the interference estimate (received signal minus desiredsignal obtained from the TSC and/or decision feedback) without requiringthe channel information of the interferer(s).

With reference now to FIG. 6, there is shown a diagram illustrating aportion of a receiver 600, wherein addition signal manipulation can beused to provide multiple additional degrees of freedom to assist ininterference cancellation, according to a preferred embodiment of thepresent invention. FIG. 6 provides a high-level view of circuitryresponsible for interference cancellation in a receiver 600. Note thatFIG. 6 does not show typical parts that may be found in a receiver, suchas an antenna, radio frequency hardware, decoding hardware, and soforth. Previously, the multiple stream input signals (the I and Qstreams) may be processed into a single stream upon interferencesuppression. The receiver 600 attempts to preserve those multiplestreams upon interference suppression. A received signal, r(t), asreceived by an antenna (not shown) of the receiver 600 and afterfiltering to remove out-of-band interferers and amplifying to bringsignal levels to a compatible level by radio frequency hardware (alsonot shown), may be sampled at a rate greater than baud-rate by asampling unit 605, meaning that the received signal, r(t), is beingoversampled. For example, the received signal may be oversampled by afactor of 2×, 4×, 6×, 8×, or any integral multiple of the baud-rate. Thesampling of the received signal creates a discrete time sequencerepresenting the received signal. After the sampling, the discrete timesequence can be provided to a space-time interference suppression unit607.

The space-time interference suppression unit 607 can include aderotation unit 610, which can be used to apply a specified rotation tothe discrete time sequence. After derotation, the discrete time sequencecan be provided to a real-valued unit 615 and an imaginary-valued unit617, which can be used for extracting the real portion and the imaginaryportion of the discrete time sequence. The real sequence can representan in-phase portion and the imaginary sequence can represent aquadrature portion of the discrete time sequence. The two sequences (thereal sequence and the imaginary sequence) can then undergo filtering bya space-time interference suppression filter 620, which makes use of amatrix filter. Note that once again, the spectral redundancy of a GMSKmodulated signal has been exploited to provide an additional degree offreedom. However, by oversampling the received signal by a factor of Q,an additional (Q-1) degrees of freedom can be provided. Furthermore, thesplitting of the discrete time sequence into two sequences doubles thedegrees of freedom to a total of 2(Q-1) degrees of freedom. The outputof the space-time interference suppression unit 607 may be colored andif the coloring is severe enough, a spatial whitening unit 650 can beused to whiten the output prior to being provided to an equalizer (notshown).

The space-time interference suppression unit 607 and the spatialwhitening unit 650 can have as input an interference suppression filtermatrix, F(z), and a spatial whitener, W, respectively. Both theinterference suppression filter matrix, F(z), and the spatial whitener,W, can be computed from the discrete time sequence and a trainingsequence of the desired user and then provided to the space-timeinterference suppression unit 607 and the spatial whitening unit 650 bya channel estimation and computing unit 622.

The channel estimation and computing unit 622 can include a channelestimation unit 625, which can compute a channel estimate, h(z), fromthe discrete time signal. The channel estimation (produced by thechannel estimation unit 625) can then be provided to a convolution unit630, wherein it can be convolved with the training code sequence of thedesired user (snapshots of which can be taken from the received signal).Output of the convolution unit 630 can be combined by a combining unit635 with the discrete time signal to produce an interference component,V(z). The interference component, V(z), can be used to compute theinterference suppression matrix, F(z), by a interference suppressionmatrix compute unit 640. The interference component, V(z), can also beused to compute the spatial whitener, W, by convolving it (via aconvolution unit 645, for example) with the interference suppressionmatrix, F(z), to produce a residual interference after interferencesuppression, e(z). The residual interference after interferencesuppression, e(z), can then be provided to a spatial whitener computeunit 655, wherein the spatial whitener, W, is computed. Note thatdetails of the computations will be discussed below. An alternativeembodiment of the receiver circuitry 600 can be found displayed in FIG.7.

Once again, to discuss the design of the receiver 600, a signal model,along with assumptions and notation shall be laid out. As discussedpreviously, a GMSK modulated signal can be approximated as:${{x(t)} = {\sum\limits_{p = {- \infty}}^{\infty}\quad{j^{p + 1}a_{p}{C_{0}\left( {t - {pT}} \right)}}}},{a_{p} \in \left\{ {\pm 1} \right\}},$wherein T is a single symbol duration and C₀(t) is the GMSK waveform ofduration 4T. Assuming that there are {tilde over (K)} co-channel usersin the communications system, the baseband receive signals can beexpressed as $\begin{matrix}{{\overset{\sim}{r}(t)} = {{\sum\limits_{p = {- \infty}}^{\infty}{j^{p + 1}a_{p}^{(1)}{{\overset{\sim}{h}}^{(1)}\left( {t - {pT}} \right)}}} + {\sum\limits_{k = 2}^{K}{\sum\limits_{p = {- \infty}}^{\infty}{j^{p + 1}a_{p}^{(k)}{{\overset{\sim}{h}}^{(k)}\left( {t - {pT}} \right)}}}} + {\overset{\sim}{n}(t)}}} \\{{= {{\sum\limits_{p = {- \infty}}^{\infty}{j^{p + 1}a_{p}^{(1)}{{\overset{\sim}{h}}^{(1)}\left( {t - {pT}} \right)}}} + {\overset{\sim}{v}(t)}}},}\end{matrix}$wherein {tilde over (h)}(t) is the overall channel impulse response,including C₀(t) with delay spread LT, ñ(t) is the thermal noise, and{tilde over (v)}(t) is the total interference plus noise.

With reference now to FIG. 8, there is shown a diagram illustrating aprocess 800 for interference cancellation in a receiver, whereinaddition signal manipulation can be used to provide multiple additionaldegrees of freedom to assist in interference cancellation, according toa preferred embodiment of the present invention. A received signal r(t),after being received by a receiver and sample (block 805) at a samplingrate Q times the received signals baud rate can be expressed as:$\begin{matrix}{{{\overset{\sim}{r}}_{m} = \begin{bmatrix}{\overset{\sim}{r}}_{Qm} \\{\overset{\sim}{r}}_{{Qm} + 1} \\\vdots \\{\overset{\sim}{r}}_{{Qm} + {({Q - 1})}}\end{bmatrix}},{{{where}\quad{\overset{\sim}{r}}_{{Qm} + q}} = {{\overset{\sim}{r}\left( \frac{\left( {{Qm} + q} \right)T}{Q} \right)}.}}} & (10)\end{matrix}$It can then be shown that $\begin{matrix}{{\overset{\sim}{r}}_{m} = {{\sum\limits_{p = {- \infty}}^{\infty}{j^{p + 1}{a_{p}^{(1)}\begin{bmatrix}{{\overset{\sim}{h}}^{(1)}\left( {\left( {m - p} \right)T} \right)} \\{{\overset{\sim}{h}}^{(1)}\left( {{\left( {m - p} \right)T} + \frac{T}{Q}} \right)} \\\vdots \\{{\overset{\sim}{h}}^{(1)}\left( {{\left( {m - p} \right)T} + {\frac{Q - 1}{Q}T}} \right)}\end{bmatrix}}}} + {\overset{\sim}{v}}_{m}}} \\{= {{\sum\limits_{p = {- \infty}}^{\infty}{j^{p + 1}a_{p}^{(1)}{\overset{\sim}{h}}_{m - p}}} + {\overset{\sim}{v}}_{m}}} \\{= {{j^{m + 1}{\sum\limits_{l = 0}^{L}{\left( {j^{- l}h_{l}^{(1)}} \right)a_{m - l}^{(1)}}}} + {{\overset{\sim}{v}}_{m}.}}}\end{matrix}$

In essence, Q times oversampling provides an additional (Q-1) degrees offreedom. Note that a_(k) in BPSK modulated, hence is real-valued. Fromequation (10), the total number of degrees of freedom can be doubled byfirst performing a derotation with a factor of (−j)^(m+1) (block 810)followed by extracting the real and imaginary parts of the resultingsignal (block 815). This can be expressible as: $\begin{matrix}\begin{matrix}{\begin{bmatrix}{{Re}\left( {j^{- {({m + 1})}}{\overset{\sim}{r}}_{m}} \right)} \\{{Im}\left( {j^{- {({m + 1})}}{\overset{\sim}{r}}_{m}} \right)}\end{bmatrix} = {{\sum\limits_{l = 0}^{L}{\begin{bmatrix}{{Re}\left( {j^{- l}{\overset{\sim}{h}}_{l}^{(1)}} \right)} \\{{Im}\left( {j^{- l}{\overset{\sim}{h}}_{i}^{(1)}} \right)}\end{bmatrix}a_{m - l}^{(1)}}} + \begin{bmatrix}{{Re}\left( {j^{- {({m + 1})}}{\overset{\sim}{v}}_{m}} \right)} \\{{Im}\left( {j^{- {({m + 1})}}{\overset{\sim}{v}}_{m}} \right)}\end{bmatrix}}} \\{{\left. \Leftrightarrow r_{m} \right. = {{\sum\limits_{l = 0}^{L}{h_{l}a_{m - l}}} + v_{m}}},}\end{matrix} & (11)\end{matrix}$wherein the superscripts (1) indicating user 1 (the desired user) aresuppressed for notational conciseness. This can then provide asingle-input 2Q-output real-valued channel. Once again, the spectralredundancy inherent in the real-valued symbol a_(k) can be exploited.

The oversample received signal r_(m) can then be processed by aspace-time interference suppression matrix filter as follows (block820): $\begin{matrix}{{y_{m} = {{\sum\limits_{n = 0}^{N}{G_{n}r_{m - n}}} = {{G_{0}r_{m}} + {\sum\limits_{n = 1}^{N}{G_{n}r_{m - n}}}}}},} & (12)\end{matrix}$wherein G_(n) ε

^(2Q×2Q) is the n-th tap of the space-time matrix filter. A detaileddiscussion of the space-time matrix filter is provided below. In thez-domain, y(z)=G(z)r(z)=G(z)(h(z)a(z)+v(z)). The processed 2Q-vectorsignal y_(m) can serve as input to a desired user equalizer of type suchas MLSE, DFE, or any other type of equalizer. The effective ISI channelfor the equalizer can be expressed as h_(eq)(z)=G(z)h(z).

The design of the filters (w₁(z) and w₂(z)) involved the use of analgorithm that maximizes SINR. A different optimization can be used todesign the space-time matrix filter G(z). The space-time matrix filter${G(z)} = {G_{0} + {\sum\limits_{n = 1}^{N}{G_{n}z^{- n}}}}$can be decomposed into two parts:

G(z)=WF(z), where $\begin{matrix}{W = {{G_{0}\quad{and}\quad{F(z)}} = {I_{2Q} + {\sum\limits_{n = 1}^{N}{F_{n}{z^{- n}.}}}}}} & (13)\end{matrix}$The following criteria can be used:

-   -   The first stage F(z) can be designed to suppress the total        interference component v(z), without affecting the desired        signal component h(z). Therefore, the first tap in F(z) may be        I_(2Q) (an identity matrix). Note that this stage may be        optional. The stage can be deactivated by setting N to 0.        Additionally, F(z) can also increase the effective channel        constraint length (before equalization) by N.    -   The second stage W may be chosen to spatially whiten the        residual interference component after space-time interference        suppression.

Using the assumption that the only available channel estimate for thedesired user is available via the use of a channel estimation algorithm,such as a single-user correlator, single-user least square, joint leastsquare, and so forth. This means that the algorithm is blind to theinterference parameters. Given that the received signal r(z) and thedesired user channel estimate ĥ(z), the interference component v(z) canbe estimated as follows:v(z)=r(z)−ĥ(z)â(z),  (14)wherein â(z) can be an estimate of the desired user data. The desireduser data can be obtained by:

-   -   v(z) can be estimated only within the mid-amble every        transmission burst. In this case, â(z) is the desired user's        training sequence, which is completely known.    -   If additional data is desired, a decision-directed approach can        be used. A preliminary data estimate (either hard or soft) can        be obtained using the output of a matched filter or even the        space-time interference suppression filter. The estimate can        then be used in conjunction with the training sequence of the        desired user to obtain a longer estimate of v(z). Alternatively,        a per-survivor processing (PSP) technique can be used to obtain        more accurate preliminary data estimates at the expense of        complexity.

The interference estimate v(z) can then be used to compute F(z)according to an optimization criterion as follows: $\begin{matrix}{{{\min\limits_{F_{1},\quad\ldots\quad,F_{N}}{\sum\limits_{m \in \Lambda}{{v_{m} + {\sum\limits_{n = 1}^{N}{F_{n}v_{m - n}}}}}^{2}}} = {\min\limits_{F_{1},\quad\ldots\quad,F_{N}}{\sum\limits_{m \in \Lambda}{e_{m}}^{2}}}},} & (15)\end{matrix}$wherein e(z)=F(z)v(z) can be the residual interference afterinterference suppression and Λ is an index set depending upon where v(z)is computed within a transmission burst. The optimization criterion inequation (15) can be viewed as a linear prediction problem. The solutioncan be obtained using any of many adaptive filtering algorithms oranalytically as follows. Let Λ={N,N+1, . . . , M} and define:${v = {\begin{bmatrix}v_{N} \\v_{N + 1} \\\vdots \\v_{M}\end{bmatrix} \in \Re^{2{Q{({M - N + 1})}}}}},{f = {{{vec}\left( \begin{bmatrix}F_{1} & F_{2} & \ldots & F_{N}\end{bmatrix} \right)} \in \Re^{4Q^{2}N}}}$ $A = {\begin{bmatrix}e_{N - 1}^{T} & e_{N - 2}^{T} & \ldots & e_{0}^{T} \\e_{N}^{T} & e_{N - 1}^{T} & \ldots & e_{1}^{T} \\\vdots & \vdots & ⋰ & \vdots \\e_{M + 1}^{T} & e_{M - 2}^{T} & \ldots & e_{M - N}^{T}\end{bmatrix} \otimes {I_{2Q}.}}$Then, the solution to equation (15) can be given as: $\begin{matrix}{f_{opt} = {{\min\limits_{f}{{v - {Af}}}^{2}} = {\left( {A^{H}\quad A} \right)^{- 1}A^{H}{v.}}}} & (16)\end{matrix}$From ƒ_(opt),${F_{opt}(z)} = {I_{2\quad Q} + {\sum\limits_{n = 1}^{N}\quad{F_{n,{opt}}z^{- n}}}}$can be obtained.

The spatial whitening transformation, W, can be obtained from theresidual interference estimate e(z)=F_(opt)(z){circumflex over (ν)}(z).First, an estimate of the spatial covariance matrix can be obtained asfollows $\begin{matrix}{{R = {\frac{1}{\Lambda }{\sum\limits_{m \in \Lambda}\quad{e_{m}e_{m}^{T}}}}},} & (17)\end{matrix}$which can then be used to derive the spatial whitening transformation:W=R_(c) ^(−1/2).  (18)Note than when N=0, e(z)=ν(z). Note also that W may be a function of aninverse of Re, i.e., the exponent of Re may have other values, such as−1 (or −⅓, −¼,− 1/5,, and so forth) instead of just −½ as shown inEquation (18) above.

For asynchronous communications systems where the interference may bepresent only within a part of a transmission burst, somedecision-directed algorithm can be used to adapt the matrix filter,G(z), to changes in the interference structure. The algorithm can startfrom mid-amble (since the desired user training sequence is known) andthen adapt from the center to the beginning and the end of eachtransmission burst. In this case, an efficient algorithm to updatematrix inverses can also be used. The decision-directed adaptivealgorithm can be based upon a host of standard adaptive filteringalgorithms, such as, NLMS and RLS (Kalman filtering).

The computation of a square-root of a matrix is needed to compute thespatial whitening transformation (see equation (18)). This can increasereceiver complexity significantly since it involves the computation of asymmetric matrix factorization. However, when an equalizer that usesmatched filtering as a front-end is used, the square-root operation maybe circumvented. In this case, the equalizer requires only the channelcorrelation estimates. The channel correlation polynomial can beexpressed as:p(z)=∥WF(z)h(z)∥² =h(z)^(T) F(z)^(T) R _(e) ⁻¹ F(z)h(z),  (19)which does not require computing the square-root of R_(e) ⁻¹. Suchsimplification can also be done for an MLSE equalizer when a front-endmatched filter is used. In this case, the branch metric definition mayneed to be modified to take into account the noise correlation aftermatched filtering FIG. 7 displays an embodiment of receiver circuitry700 that takes advantage of this simplification.

After interference suppression (filtering and combining) and beforebeing provided to an MLSE equalizer, it may be necessary to whiten(de-correlate) the interference suppressed signal (block 825). Theinterference suppressed signal can be severely colored (as a result ofthe filtering and combining operations). If this is the case, then theinterference suppressed signal can be whitened (for example by awhitening unit 650 (FIG. 6)). Severe coloring can impact the performanceof MLSE. The whitening can be implemented adaptively using linearprediction filtering. Note that the process 800 may not be limited toco-channel interference suppression alone, the process 800 can alsosuppress adjacent channel interference.

With reference now to FIG. 9, there is shown a data plot illustrating alink level performance comparison of several single antenna interferencecancellation algorithms in an environment with a single co-channelinterferer, according to a preferred embodiment of the presentinvention. A first curve 905 displays the performance of a conventionalMLSE algorithm, a second curve 910 displays the performance of a MLSEalgorithm with pre-whitening prior to MLSE, a third curve 915 displaysthe performance of a successive interference canceling algorithm, afourth curve 920 displays the performance of a joint MLSE algorithm, afifth curve 925 displays the performance of an embodiment of theproposed interference suppression scheme (as displayed in FIG. 5) withno whitening and a sixth curve 930 displays the performance of anembodiment of the proposed interference suppression scheme (as displayedin FIG. 5) with whitening. The performance results show that the jointMLSE algorithm outperforms the proposed interference suppression schemewith and without whitening. Note however, that the proposed interferencesuppression schemes permit a blind implementation, which is not possiblewith joint MLSE or successive interference cancellation.

With reference now to FIG. 10, there is shown a data plot illustrating alink level performance comparison of several single antenna interferencecancellation algorithms (with and without oversampling) in anenvironment with a single co-channel interferer, according to apreferred embodiment of the present invention. A first and second curve1005 and 1010 display the performance of conventional MLSE withoversampling factors of one (1) and two (2), a third and fourth curve1015 and 1020 display the performance of an embodiment of the presentinvention (as displayed in FIG. 8, with N=0) with oversampling factorsof one and two, a fifth and sixth curve 1025 and 1030 display theperformance of an embodiment of the present invention (as displayed inFIG. 8, with N=1) with over sampling factors of one and two. Theperformance results show that 2× oversampling can provide significantgait over baud-rate sampling in the embodiments of the presentinvention. However, oversampling does not provide a performance gain forconventional MLSE receiver.

Although the present and its advantages have been described in detail,it should be understood that various changes, substitutions andalterations can be made herein without departing from the spirit andscope of the invention as defined by the appended claims.

Moreover, the scope of the present application is not intended to belimited to the particular embodiments of the process, machine,manufacture, composition of matter, means, methods and steps describedin the specification. As one of ordinary skill in the art will readilyappreciate from the disclosure of the present invention, processes,machines, manufacture, compositions of matter, means, methods, or steps,presently existing or later to be developed, that perform substantiallythe same function or achieve substantially the same result as thecorresponding embodiments described herein may be utilized according tothe present invention. Accordingly, the appended claims are intended toinclude within their scope such processes, machines, manufacture,compositions of matter, means, methods, or steps.

1-38. (canceled)
 39. A method for suppressing interference in a receivedsignal comprising: sampling the received signal to create a discretetime sequence representing the received signal; rotating the discretetime sequence by a specified amount; extracting in-phase and quadraturephase streams from the rotated, sampled received signal; applying aninterference suppression filter to the in-phase and quadrature phasestreams; and combining the filtered in-phase and quadrature phasestreams.
 40. The method of claim 39 further comprising after thecombining, whitening the combined streams.
 41. The method of claim 40,wherein the whitening can be performed via a temporal filter.
 42. Themethod of claim 41, wherein the temporal filter can be implemented via alinear predictor.
 43. The method of claim 39, wherein the sampling is ata sampling rate essentially equal to a symbol rate of the receivedsignal.
 44. The method of claim 39, wherein the in-phase stream is thereal portion of the rotated, sampled received signal and the quadraturestream is the imaginary portion of the rotated, sampled received signal.45. The method of claim 43, wherein the interference suppression filtercan be designed using a zero-forcing criteria.
 46. The method of claim43, wherein the interference suppression filter can be designed using aminimum mean square error criteria.
 47. The method of claim 43, whereinthe interference suppression filter can be designed using a maximumsignal to interference plus noise (SINR) criteria.
 48. The method ofclaim 34, wherein the sampling is at a sampling rate that is greaterthan a symbol rate of the received signal.
 49. A method for suppressinginterference in a received signal comprising: sampling the receivedsignal to create a discrete time sequence representing the receivedsignal, wherein the sampling is at a sampling rate that is not less thana symbol rate of the received signal; rotating the discrete timesequence by a specified amount; extracting in-phase and quadrature phasestreams from the rotated, sample received signal; and applying aninterference suppression filter to the in-phase and quadrature phasestreams.
 50. The method of claim 49 further comprising after theapplying, whitening the in-phase and quadrature phase streams.
 51. Themethod of claim 50, wherein the whitening can be performed by a spatialwhitening transform, W, and wherein the spatial whitening transform is afunction of an inverse of an interference convariance matrix estimate,wherein:${R = {\frac{1}{\Lambda }{\sum\limits_{m \in \Lambda}\quad{e_{m}e_{m}^{T}}}}},$wherein e(z)=F(z)v(z) can be the residual interference afterinterference suppression, F(z) is the interference suppression matrix, Λis an index set depending upon where v(z) is computed within atransmission burst, and R is the interference covariance matrixestimate.
 52. The method of claim 51, wherein the covariance matrixestimates can be derived from transmission training sequences.
 53. Themethod of claim 51, wherein the covariance matrix estimates can bederived via decision feedback.
 54. The method of claim 51, whereinW=R_(e) ⁻¹.
 55. The method of claim 51, wherein W=R_(e) ^(−1/2).
 56. Themethod of claim 51, wherein the sampling is at a sampling rate that isgreater than a symbol rate of the received signal.
 57. A circuitcomprising: a sampling and coupled to a signal input, the sampling unitcontaining circuitry to sample a received signal provided by the signalinput at a specified sampling rate and to create a discrete timesequence representing the received signal; a rotating unit coupled tothe sampling unit, the rotating and containing circuitry to rotate thediscrete time sequence by a specified amount; a pair of extractorscoupled to the rotating unit, the extractors containing circuitry toextract an in-phase and a quadrature phase steam from an output of therotating unit; and a filter coupled to the pair of extractors, thefilter containing circuitry to suppress interference present in thereceived signal.
 58. The circuit of claim 57 further comprising awhitening unit coupled to the filter, the whitening unit containingcircuitry to de-correlate information present in the output of thefilter.
 59. The circuit of claim 57, wherein the filter comprises: apair of filters, each filter to be applied separately to the in-phaseand the quadrature phage streams; and a combiner coupled to the pair offilters, the combiner to sum the outputs from the pair of filters. 60.The circuit of claim 57, wherein the filter is a space-time interferencesuppression filter (STISF).
 61. The circuit of claim 60 furthercomprising a whitening unit coupled to the STISF containing circuitry tode-correlate information present in the output of the STISF, wherein atransfer function of the STISF is computed from the output of the pairof extractors and captured training sequences from transmissions of adesired user.
 62. The circuit of claim 61, wherein the transfer functionis the sum of the output of the pair of extractors and a convolution ofa channel estimate with the captured training sequences.
 62. The circuitof claim 61, wherein the whitening unit applies a convolution of thetransfer function of the STISF with a sum of the output of the pair ofextractors and a convolution of a channel estimate with the capturedtraining sequences.